Iterative Maximum Likelihood Algorithms
Iterative Maximum Likelihood Algorithms is a computational methodology within statistical inference that iteratively refines parameter estimates to maximize the likelihood function through successive approximations, distinguishing itself from closed-form solutions by its reliance on stepwise convergence to the optimal estimate.
367
Publications
29K
Citations
859
Authors
418
Institutions
Publications per year
2017–2026
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University of Southern California
Los Angeles, United States
9 h-index
23 publications
1.3K citations
University of California, Berkeley
Berkeley, United States
8 h-index
24 publications
2.6K citations
Harvard University
Cambridge, United States
7 h-index
8 publications
843 citations
University of Utah
Salt Lake City, United States
6 h-index
8 publications
506 citations

Tel Aviv University
Tel Aviv, Israel
6 h-index
8 publications
267 citations
University of Washington
Seattle, United States
6 h-index
7 publications
1K citations
University of Massachusetts Chan Medical School
Worcester, United States
5 h-index
10 publications
388 citations

Centre National de la Recherche Scientifique
Paris, France
5 h-index
11 publications
430 citations
1–8 of 418
28 publications · 1.5K citations
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21 publications · 4.7K citations
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10 publications · 1.3K citations
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10 publications · 1K citations
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9 publications · 588 citations
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